Standing on the shoulders of ants : stigmergy in the Web

Stigmergy is a biological term used when discussing insect or swarm behaviour, and describes a model supporting environmental communication separately from artefacts or agents. This phenomenon is demonstrated in the behavior of ants and their food gathering process when following pheromone trails, or similarly termites and their termite mound building process. What is interesting with this mechanism is that highly organized societies are achieved with a lack of any apparent management structure. Stigmergic behavior is implicit in the Web where the volume of users provides a self-organizing and self-contextualization of content in sites which facilitate collaboration. However, the majority of content is generated by a minority of the Web participants. A significant contribution from this research would be to create a model of Web stigmergy, identifying virtual pheromones and their importance in the collaborative process. This paper explores how exploiting stigmergy has the potential of providing a valuable mechanism for identifying and analyzing online user behavior recording actionable knowledge otherwise lost in the existing web interaction dynamics. Ultimately this might assist our building better collaborative Web sites.

Stigmergy within web modelling languages : positive feedback mechanisms

Stigmergy is a biological term originally used when discussing insect or swarm behaviour, and describes a model supporting environment-based communication separating artefacts from agents. This phenomenon is demonstrated in the behavior of ants and their food foraging supported by pheromone trails, or similarly termites and their termite nest building process. What is interesting with this mechanism is that highly organized societies are formed without an apparent central management function. We see design features in Web sites that mimic stigmergic mechanisms as part of the User Interface and we have created generalizations of these patterns. Software development and Web site development techniques have evolved significantly over the past 20 years. Recent progress in this area proposes languages to model web applications to facilitate the nuances specific to these developments. These modeling languages provide a suitable framework for building reusable components encapsulating our design patterns of stigmergy. We hypothesize that incorporating stigmergy as a separate feature of a site’s primary function will ultimately lead to enhanced user coordination.

General theory of stigmergy : modelling stigma semantics

Stigmergy is a biological term used when discussing a sub-set of insect swarm-behaviour describing the apparent organisation seen during their activities. Stigmergy describes a communication mechanism based on environment-mediated signals which trigger responses among the insects. This phenomenon is demonstrated in the behavior of ants and their food gathering process when following pheromone trails, where the pheromones are a form of environment-mediated communication. What is interesting with this phenomenon is that highly organized societies are achieved without an apparent management structure. Stigmergy is also observed in human environments, both natural and engineered. It is implicit in the Web where sites provide a virtual environment supporting coordinative contributions. Researchers in varying disciplines appreciate the power of this phenomenon and have studied how to exploit it. As stigmergy becomes more widely researched we see its definition mutate as papers citing original work become referenced themselves. Each paper interprets these works in ways very specific to the research being conducted. Our own research aims to better understand what improves the collaborative function of a Web site when exploiting the phenomenon. However when researching stigmergy to develop our understanding we discover a lack of a standardized and abstract model for the phenomenon. Papers frequently cited the same generic descriptions before becoming intimately focused on formal specifications of an algorithm, or esoteric discussions regarding sub-facets of the topic. None provide a holistic and macro-level view to model and standardize the nomenclature. This paper provides a content analysis of influential literature documenting the numerous theoretical and experimental papers that have focused on stigmergy. We establish that stigmergy is a phenomenon that transcends the insect world and is more than just a metaphor when applied to the human world. We present from our own research our general theory and abstract model of semantics of stigma in stigmergy. We hope our model will clarify the nuances of the phenomenon into a useful road-map, and standardise vocabulary that we witness becoming confused and divergent. Furthermore, this paper documents the analysis on which we base our next paper: Special Theory of Stigmergy: A Design Pattern for Web 2.0 Collaboration.

Collaboration in Web N.0: Stigmergy and virtual pheromones

The aim of this project was to develop a general theory of stigmergy and a software design pattern to build collaborative websites. Stigmergy is a biological term used when describing some insect swarm-behaviour where 'food gathering' and 'nest building' activities demonstrate the emergence of self-organised societies achieved without an apparent management structure. The results of the project are an abstract model of stigmergy and a software design pattern for building Web 2.0 components exploiting this self-organizing phenomenon. A proof-of-concept implementation was also created demonstrating potential commercial viability for future website projects.

Convergence results for stochastic particle systems with social interaction

We consider stochastic individual-based models for social behaviour of groups of animals. In these models the trajectory of each animal is given by a stochastic differential equation with interaction. The social interaction is contained in the drift term of the SDE. We consider a global aggregation force and a short-range repulsion force. The repulsion range and strength gets rescaled with the number of animals N. We show that for N tending to infinity stochastic fluctuations disappear and a smoothed version of the empirical process converges uniformly towards the solution of a nonlinear, nonlocal partial differential equation of advection-reaction-diffusion type. The rescaling of the repulsion in the individual-based model implies that the corresponding term in the limit equation is local while the aggregation term is non-local. Moreover, we discuss the effect of a predator on the system and derive an analogous convergence result. The predator acts as an repulsive force. Different laws of motion for the predator are considered.

Collision avoidance between UAV clusters using swarm intelligence techniques

In this article, several basic swarming laws for Unmanned Aerial Vehicles (UAVs) are developed for both two-dimensional (2D) plane and three-dimensional (3D) space. Effects of these basic laws on the group behaviour of swarms of UAVs are studied. It is shown that when cohesion rule is applied an equilibrium condition is reached in which all the UAVs settle at the same altitude on a circle of constant radius. It is also proved analytically that this equilibrium condition is stable for all values of velocity and acceleration. A decentralised autonomous decision-making approach that achieves collision avoidance without any central authority is also proposed in this article. Algorithms are developed with the help of these swarming laws for two types of collision avoidance, Group-wise and Individual, in 2D plane and 3D space. Effect of various parameters are studied on both types of collision avoidance schemes through extensive simulations.

Antipredator behaviour of Baltic planktivores

Predation is an important source of mortality for most aquatic animals. Thus, the ability to avoid being eaten brings substantial fitness benefits to individuals. Predator detection abilities and antipredator behaviour were examined in various planktivores, i.e. the littoral mysids Neomysis integer and Praunus flexuosus, three-spined stickleback Gasterosteus aculeatus larvae, pelagic mysids Mysis mixta and M. relicta, and the predatory cladoceran Cercopagis pengoi, with cues from their respective predators European perch Perca fluviatilis and Baltic herring Clupea harengus membras. The use of different aquatic macrophytes as predation refuges by the littoral planktivores was also examined. All pelagic planktivores and stickleback larvae were able to detect the presence of their predator by chemical cues alone. The littoral mysids N. integer and P. flexuosus responded only when chemical and visual predator cues were combined. The responses of stickleback larvae were stronger to the combined cues than the chemical cue alone. A common antipredator behaviour in all of the planktivores studied was decreased ingestion rate in response to predator cues. N. integer and stickleback larvae also decreased their swimming activity. Pelagic mysids and C. pengoi altered their prey selectivity patterns in response to predator cues. The effects of predator cues on the swarming behaviour of N. integer were examined. Swarming brings clear antipredator advantages to N. integer, since when they feed in a swarm, they do not significantly decrease their feeding rate. However, the swarming behaviour of N. integer was not affected by predation risk, but was instead a fixed strategy. Despite the presence or absence of predator cues, N. integer individuals attempted to associate with a swarm and preferred larger to smaller swarms. In studies with aquatic macrophytes, stickleback larvae and P. flexuosus utilized vegetation as a predation refuge, spending more time within vegetation when under predation threat. The two macroalgal species studied, bladderwrack Fucus vesiculosus and stonewort Chara tomentosa, were preferred by P. flexuosus, whereas Eurasian watermilfoil Myriophyllum spicatum was strongly avoided by N. integer and stickleback larvae. In fact, when in dense patches in aquaria, M. spicatum caused acute and high mortality (> 70%) in littoral mysids, but not in sticklebacks, whereas C. tomentosa and northern watermilfoil M. sibiricum did not. In contrast, only 2-4% mortality in N. integer was observed with intact and broken stems of M. spicatum in field experiments. The distribution of littoral mysids in different vegetations, however, suggests that N. integer avoids areas vegetated by M. spicatum.

A model for heat transfer in a honey bee swarm

A swarm is a temporary structure formed when several thousand honey bees leave their hive and settle on some object such as the branch of a tree. They remain in this position until a suitable site for a new home is located by the scout bees. A continuum model based on heat conduction and heat generation is used to predict temperature profiles in swarms. Since internal convection is neglected, the model is applicable only at low values of the ambient temperature T-a. Guided by the experimental observations of Heinrich (1981a-c, J. Exp. Biol. 91, 25-55; Science 212, 565-566; Sci. Am. 244, 147-160), the analysis is carried out mainly for non-spherical swarms. The effective thermal conductivity is estimated using the data of Heinrich (1981a, J. Exp. Biol. 91, 25-55) for dead bees. For T-a = 5 and 9 degrees C, results based on a modified version of the heat generation function due to Southwick (1991, The Behaviour and Physiology of Bees, PP 28-47. C.A.B. International, London) are in reasonable agreement with measurements. Results obtained with the heat generation function of Myerscough (1993, J. Theor. Biol. 162, 381-393) are qualitatively similar to those obtained with Southwick's function, but the error is more in the former case. The results suggest that the bees near the periphery generate more heat than those near the core, in accord with the conjecture of Heinrich (1981c, Sci. Am. 244, 147-160). On the other hand, for T-a = 5 degrees C, the heat generation function of Omholt and Lonvik (1986, J. Theor. Biol. 120, 447-456) leads to a trivial steady state where the entire swarm is at the ambient temperature. Therefore an acceptable heat generation function must result in a steady state which is both non-trivial and stable with respect to small perturbations. Omholt and Lonvik's function satisfies the first requirement, but not the second. For T-a = 15 degrees C, there is a considerable difference between predicted and measured values, probably due to the neglect of internal convection in the model.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Optimisation of a fermentation process for butanol production by particle swarm optimisation (PSO)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Division of labour in a group of agents inspired by ants' foraging behaviour

Questa tesi prende spunto da altri studi realizzati nel campo delle esattamente nel campo delle “Swam Intelligence”, una branca delle intelligenze artificiali prende spunto dal comportamento di animali sociali, sopratutto insetti come termini, formiche ed api, per trarne interessanti metafore per la creazione di algoritmi e tecniche di programmazione. Questo tipo di algoritmi, come per gli esempi tratti dalla biologia, risultano dotati di interessanti proprietà adatte alla risoluzione di certi problemi nell'ambito dell'ingegneria. Lo scopo della tesi è quello di mostrare tramite un esempio pratico le proprietà dei sistemi sviluppati tramite i principi delle Swarm Intelligence, evidenziando la flessibilità di questi sistemi. Nello specifico, la mia tesi analizzerà il problema della suddivisione del lavoro in una colonia di formiche, fornendo un esempio pratico quale il compito di cattura di prede in un determinato ambiente. Ho sviluppato un'applicazione software in Java che simula tale comportamento, i dati utilizzati durante le diverse simulazioni possono essere modificati tramite file di testo, in modo da ottenere risultati validi per diversi contesti.

Coordination and Control of Autonomous Mobile Robots Swarms by using Particle Swarm Optimization Algorithm and Consensus Theory

This thesis presents some different techniques designed to drive a swarm of robots in an a-priori unknown environment in order to move the group from a starting area to a final one avoiding obstacles. The presented techniques are based on two different theories used alone or in combination: Swarm Intelligence (SI) and Graph Theory. Both theories are based on the study of interactions between different entities (also called agents or units) in Multi- Agent Systems (MAS). The first one belongs to the Artificial Intelligence context and the second one to the Distributed Systems context. These theories, each one from its own point of view, exploit the emergent behaviour that comes from the interactive work of the entities, in order to achieve a common goal. The features of flexibility and adaptability of the swarm have been exploited with the aim to overcome and to minimize difficulties and problems that can affect one or more units of the group, having minimal impact to the whole group and to the common main target. Another aim of this work is to show the importance of the information shared between the units of the group, such as the communication topology, because it helps to maintain the environmental information, detected by each single agent, updated among the swarm. Swarm Intelligence has been applied to the presented technique, through the Particle Swarm Optimization algorithm (PSO), taking advantage of its features as a navigation system. The Graph Theory has been applied by exploiting Consensus and the application of the agreement protocol with the aim to maintain the units in a desired and controlled formation. This approach has been followed in order to conserve the power of PSO and to control part of its random behaviour with a distributed control algorithm like Consensus.

Virtual sensing technology applied to a swarm of autonomous robots.

This thesis proposes a novel technology in the field of swarm robotics that allows a swarm of robots to sense a virtual environment through virtual sensors. Virtual sensing is a desirable and helpful technology in swarm robotics research activity, because it allows the researchers to efficiently and quickly perform experiments otherwise more expensive and time consuming, or even impossible. In particular, we envision two useful applications for virtual sensing technology. On the one hand, it is possible to prototype and foresee the effects of a new sensor on a robot swarm, before producing it. On the other hand, thanks to this technology it is possible to study the behaviour of robots operating in environments that are not easily reproducible inside a lab for safety reasons or just because physically infeasible. The use of virtual sensing technology for sensor prototyping aims to foresee the behaviour of the swarm enhanced with new or more powerful sensors, without producing the hardware. Sensor prototyping can be used to tune a new sensor or perform performance comparison tests between alternative types of sensors. This kind of prototyping experiments can be performed through the presented tool, that allows to rapidly develop and test software virtual sensors of different typologies and quality, emulating the behaviour of several hardware real sensors. By investigating on which sensors is better to invest, a researcher can minimize the sensors’ production cost while achieving a given swarm performance. Through augmented reality, it is possible to test the performance of the swarm in a desired virtual environment that cannot be set into the lab for physical, logistic or economical reasons. The virtual environment is sensed by the robots through properly designed virtual sensors. Virtual sensing technology allows a researcher to quickly carry out real robots experiment in challenging scenarios without all the required hardware and environment.

The Optimal combination: Grammatical Swarm, Particle Swarm Optimization and Neural Networks.

Social behaviour is mainly based on swarm colonies, in which each individual shares its knowledge about the environment with other individuals to get optimal solutions. Such co-operative model differs from competitive models in the way that individuals die and are born by combining information of alive ones. This paper presents the particle swarm optimization with differential evolution algorithm in order to train a neural network instead the classic back propagation algorithm. The performance of a neural network for particular problems is critically dependant on the choice of the processing elements, the net architecture and the learning algorithm. This work is focused in the development of methods for the evolutionary design of artificial neural networks. This paper focuses in optimizing the topology and structure of connectivity for these networks.

Polynomial approximation using particle swarm optimization of lineal enhanced neural networks with no hidden layers.

This paper presents some ideas about a new neural network architecture that can be compared to a Taylor analysis when dealing with patterns. Such architecture is based on lineal activation functions with an axo-axonic architecture. A biological axo-axonic connection between two neurons is defined as the weight in a connection in given by the output of another third neuron. This idea can be implemented in the so called Enhanced Neural Networks in which two Multilayer Perceptrons are used; the first one will output the weights that the second MLP uses to computed the desired output. This kind of neural network has universal approximation properties even with lineal activation functions. There exists a clear difference between cooperative and competitive strategies. The former ones are based on the swarm colonies, in which all individuals share its knowledge about the goal in order to pass such information to other individuals to get optimum solution. The latter ones are based on genetic models, that is, individuals can die and new individuals are created combining information of alive one; or are based on molecular/celular behaviour passing information from one structure to another. A swarm-based model is applied to obtain the Neural Network, training the net with a Particle Swarm algorithm.